The TF–IDF (term frequency–inverse document frequency) weight is a well-known indexing weight in information retrieval and text mining. However, it is not suitable for the increasingly popular voice-to-text search, as it does not take into account the impact of voice in the search process. We propose a method for calculating a new indexing weight, which is used as guidance for selection of suit...

The need for an efficient method to find the furthermost appropriate document corresponding a particular search query has become crucial due exponential development in number of papers that are now readily available us on web. vector space model (VSM) perfect used “information retrieval”, represents these words as and gives them weights via popular weighting known term frequency inverse (TF-IDF...

This paper deals with analysis and comparison of two well-known content-based recommendation approaches for scientific papers in biomedical domain. Given a rich set of abstracts for thousands of articles from PUBMED, a series of efficient pre-processing techniques are proposed. Then, for the first approach, a Term-frequency Inverse-document-frequency (TF-IDF) algorithm is formulated to make rec...

The process of text categorization involves some understanding of the content of the documents and/or some previous knowledge of the categories. For the content of the documents, we use a filtering measure for feature selection in our Chinese text categorization system. We modify the formula of Term Frequency-Inverse Document Frequency (TF-IDF) to strengthen important keywords’ weights and weak...

-Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker .It is used to identify the mood emotional tone of the speaker. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain the idea of the public. The problem is hard to find...

This paper explores the application of probabilistic information retrieval theories to the field of log analysis and host-based intrusion detection. Strong similarities exist between intrusion detection and information retrieval. Using information retrieval techniques may yield significant improvements to the performance of intrusion detection systems. This paper provides a brief review of curr...

In this paper, we investigate whether a semantic representation of patent documents provides added value for multi-dimensional visual exploration landscape compared to traditional approaches that use tf–idf (term frequency–inverse document frequency). Word embeddings from pre-trained word2vec model created text are used calculate pairwise similarities in order represent each the space. Then, hi...

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't what makes those fake news spread. The threat grows, and some point, it gets out hand. That's why this paper is trying to figure how predict the features make It's using Naive Bayes, Logistic Regression, Random forest Machine Learning do this. Online stories were made up used. They found by...

A novel approach for the automated detection of profanity in English audio songs using machine learning techniques. One primary drawbacks existing systems is only confined to textual data. The proposed method utilizes a combination feature extraction techniques and algorithms identify songs. Specifically, employs popular Term frequency–inverse document frequency (TF-IDF), Bidirectional Encoder ...